how to constrain scipy curve_fit in positive result












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I'm using scipy curve_fit to curve a line for retention. however, I found the result line may produce negative number. how can i add some constrain?
the 'bounds' only constrain parameters not the results y






python scipy







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    I'm using scipy curve_fit to curve a line for retention. however, I found the result line may produce negative number. how can i add some constrain?
    the 'bounds' only constrain parameters not the results y






    python scipy







    share|improve this question







    New contributor




    dongcoder7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.























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      0







      I'm using scipy curve_fit to curve a line for retention. however, I found the result line may produce negative number. how can i add some constrain?
      the 'bounds' only constrain parameters not the results y






      python scipy







      share|improve this question







      New contributor




      dongcoder7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I'm using scipy curve_fit to curve a line for retention. however, I found the result line may produce negative number. how can i add some constrain?
      the 'bounds' only constrain parameters not the results y






      python scipy




      python scipy






      share|improve this question







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      dongcoder7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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          One of the simpler ways to handle negative value in y, is to make a log transformation. Get the best fit for log transformed y, then do exponential transformation for actual error in the fit or for any new value prediction.






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            One of the simpler ways to handle negative value in y, is to make a log transformation. Get the best fit for log transformed y, then do exponential transformation for actual error in the fit or for any new value prediction.






            share|improve this answer


























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              One of the simpler ways to handle negative value in y, is to make a log transformation. Get the best fit for log transformed y, then do exponential transformation for actual error in the fit or for any new value prediction.






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                One of the simpler ways to handle negative value in y, is to make a log transformation. Get the best fit for log transformed y, then do exponential transformation for actual error in the fit or for any new value prediction.






                share|improve this answer












                One of the simpler ways to handle negative value in y, is to make a log transformation. Get the best fit for log transformed y, then do exponential transformation for actual error in the fit or for any new value prediction.







                share|improve this answer












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                answered 15 hours ago









                AI_Learning

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